From Chatter To Conversation

Vanessa Bugasch SVP, Global Marketing & Product Marketing, Cision

Everyone is buzzing about “big data” and how it can provide the insight needed to make better business decisions. As marketers, one of the ways we encounter big data is through the tons of chatter created in the social-media universe. How can we sort through all of this content to uncover exactly who is shaping opinion on products, brands, and companies?

Initially, automation tools helped marketers aggregate the vast amount of social-media data from various channels so it was possible to monitor what was being said and begin to engage with audiences. This remains a critical component of any organization’s social-media strategy, but now we need to take it to the next level and identify strategic opportunities from all of this content. Understanding who is influencing online conversations, as well as how to engage with those influencers, is critical to effective management of the marketing mix.

A new generation of marketing-automation applications integrates social-media monitoring and influence-rating data. What challenges do they address, and how can these tools be used most effectively?

Sifting Through The Content
In big data’s raw, unfiltered state, a lot of junk emerges from social-media conversations—idle chatter, irrelevant comments, and plain old spam. But there are also miniblogs and niche blogs that could be highly important to a brand. So it’s best to start by casting the widest possible net to aggregate all potential sources of online influence—as long as you eliminate obvious spam and scrub clearly erroneous or extraneous returns from the next phase of the search. You want to start examining conversations beyond just your brand and your competitors', but let’s be practical: You can’t read every tweet, comment, review, and blog post when there are thousands and thousands of articles to sift through.

On the scene for years, natural-language processing (NLP) and the semantic Web look like they might be finally poised to make an impact on mining online conversations and content for maximum relevancy. By identifying relationships between words and phrases, NLP helps identify the most meaningful results for online searching and social-media monitoring. NLP can help you learn who’s talking about a particular topic or trend at a particular moment and how engaged people are in the conversation. It can also help you understand the overall tone of the conversations. Though we know that a machine is never going to be 100 percent accurate in determining tone, the accuracy of NLP tools continues to improve and can certainly identify overall trends.

Imagine, then, that you release a new product in the marketplace on Monday, and by Tuesday you can provide a report that indicates how well that product was received by the social sphere and what the tone of the conversation was. Take it a step further and imagine that you can identify who was most actively engaged in discussing the brand and how they influenced the conversation. That’s what NLP can do for you.  

Not All Influence Is Created Equal
For marketers, the notion of segmentation is nothing new—but how can we better segment our social-media audience? In many cases, we don’t have our traditional segmentation data, such as title, company, industry, or even location. So how can we help narrow our focus when targeting online influencers?

In determining a 360-degree measurement of influence, the more relevant criteria used, the better. The starting point is basic: Twitter followers, Facebook fans, and LinkedIn connections. But then consider the dozens of variables: unique visitors, page views, time spent on site and comments, overall social presence (many influencers contribute to multiple online sites and social platforms) and frequency of content sharing, and audience and circulation reach. Understanding local influence is also critical. With data from QR codes and location tags now offering insight into where people are interacting with brands, marketers need to understand how to build relationships and social capital with influencers in those geographies.

Influencer Research Drives Productive Engagement
Engaging with any influencer requires preparation because traditional marketing methods no longer resonate with the social universe. The more you can learn about your target audience, the better your chances are of creating a productive relationship or engagement. But the explosion in the sheer number of influencers—from about 1 million journalists, analysts, and bloggers just a few years ago to more than 30 million (and climbing) mass influencers and opinionated consumers—has also become a big-data challenge.

The good news: Tools can help you aggregate all the data about an influencer so you can do your research. Learning about their blogs, recent tweets, Facebook updates, LinkedIn background, etc., can provide insight into what interests them—and, more important, demonstrates to influencers that you’re not just blindly spamming them.

But let’s be honest. You have a tough time keeping up on your own Facebook wall, let alone reading the status updates of every potential influencer for your product. Again, tools that can help aggregate the data and identify trends, tag topics, and group influencers by category can save you vast amounts of time and give you leg up on the competition.

Once you’ve identified the appropriate influencers, you can use the intelligence from big data to better understand what they write about, what they are working on, their pet peeves, their backgrounds, and more so you can map out a plan for engagement.

A Continuous Cycle
The challenges of filtering online noise, identifying influencers, quantifying influence, and analyzing and reacting (appropriately) to hundreds of millions of social messages every day are just the first steps in an ongoing process. The signals and hidden insights within social-media conversations remain to be mined, and because the dynamics of influence continually shift, the universe of people that we engage online will change almost daily.

But the interaction is often exhilarating, the results are critical, and the rewards are as massive as big data itself.

About Vanessa Bugasch

Vanessa Bugasch has overall responsibility for Marketing and Product Marketing for Cision worldwide. Prior to joining Cision in May 2005, she was the regional director of business development for PeopleSoft's education group. She has held various management positions in client services, training, and business development areas at several companies, including Oracle and ADP.